Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Radar image segmentation using recurrent artificial neural networks
Pattern Recognition Letters - Special issue on neural networks for computer vision applications
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Document image segmentation using discriminative learning over connected components
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model
IEEE Transactions on Image Processing
Competitive neural trees for pattern classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Taking cue from the fact of Context-dependence in Human Cognition process, in this work an image segmentation method is introduced where each pixel are classified into its object-class depending on properties of its neighboring pixels within a context-window-frame surrounding it. In brief methodological steps, the convolution of array obtained as intensities of pixels of a context window is done with weights obtained through a specific architecture of Artificial Neural Network after training. The result of convolution is utilized to define class of an object. The training set of pixels is selected judiciously considering exhaustive variety of context-types which includes pixels inside, outside, boundary of objects. This work also gives a novel approach for quantitative assessment of segmentation-efficiency for a segmentation process. Also the use of context-window appears to improve the segmentation process because of equivalence of this approach with those which use a combination of local texture and color for segmentation.